RRepoGEO

REPOGEO REPORT · LITE

EmbraceAGI/Awesome-AGI

Default branch main · commit 4d4f094c · scanned 6/3/2026, 6:58:15 PM

GitHub: 573 stars · 54 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface EmbraceAGI/Awesome-AGI, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify repo's nature

    Why:

    CURRENT
    A curated list of awesome AGI frameworks, software, and resources.
    COPY-PASTE FIX
    This repository is a curated list and comprehensive index of awesome AGI frameworks, software, and resources. It serves as a central hub for exploring the rapidly evolving field of Artificial General Intelligence.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/EmbraceAGI/Awesome-AGI

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface EmbraceAGI/Awesome-AGI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. TensorFlow · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for developing artificial general intelligence applications?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. PyTorch Lightning
    3. Hugging Face Transformers
    4. TensorFlow
    5. Keras
    6. TensorFlow Probability
    7. TensorFlow Agents
    8. JAX
    9. OpenAI Gym
    10. Gymnasium
    11. DeepMind Lab
    12. AlphaFold
    13. AlphaGo
    14. MuZero
    15. SWI-Prolog
    16. Clingo

    AI recommended 16 alternatives but never named EmbraceAGI/Awesome-AGI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources and software for autonomous agent development?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
    3. DeepMind Lab (deepmind/lab)
    4. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    5. ROS (Robot Operating System)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch (pytorch/pytorch)
    8. Stable Baselines3 (DLR-RM/stable-baselines3)
    9. Mojo (Programming Language)

    AI recommended 9 alternatives but never named EmbraceAGI/Awesome-AGI. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of EmbraceAGI/Awesome-AGI?
    pass
    AI did not name EmbraceAGI/Awesome-AGI — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts EmbraceAGI/Awesome-AGI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EmbraceAGI/Awesome-AGI explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo EmbraceAGI/Awesome-AGI solve, and who is the primary audience?
    pass
    AI did not name EmbraceAGI/Awesome-AGI — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of EmbraceAGI/Awesome-AGI. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/EmbraceAGI/Awesome-AGI.svg)](https://repogeo.com/en/r/EmbraceAGI/Awesome-AGI)
HTML
<a href="https://repogeo.com/en/r/EmbraceAGI/Awesome-AGI"><img src="https://repogeo.com/badge/EmbraceAGI/Awesome-AGI.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

EmbraceAGI/Awesome-AGI — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite